qstock由“Python金融量化”公众号开发，试图打造成个人量化投研分析开源库，目前包括数据获取（data）、可视化(plot)、选股(stock)和量化回测（backtest）四个模块。其中数据模块（data）数据来源于东方财富网、同花顺、新浪财经等网上公开数据，数据爬虫部分参考了现有金融数据包tushare、akshare和efinance。qstock致力于为用户提供更加简洁和规整化的金融市场数据接口。可视化模块基于plotly.express和pyecharts包，为用户提供基于web的交互图形简单操作接口；选股模块提供了同花顺的技术选股和公众号策略选股，包括RPS、MM趋势、财务指标、资金流模型等，回测模块为大家提供向量化（基于pandas）和基于事件驱动的基本框架和模型。
qstock目前在pypi官网上发布，开源第一版本为1.1.0，目前更新至1.3.4.
读者直接“pip install qstock ”安装，或通过'pip install --upgrade qstock'进行更新。GitHub地址：https://github.com/tkfy920/qstock。
目前部分策略选股和策略回测功能仅供知识星球会员使用，会员可在知识星球置顶帖子上上获取qstock离线安装包。
相关教程：
【qstock开源了】数据篇之行情交易数据(https://mp.weixin.qq.com/s/p1lEsBVhrm5ej3YZl0BhOw)
【qstock数据篇】行业概念板块与资金流(https://mp.weixin.qq.com/s/hj4yuqFDBidAeSRucz6TwQ)
【qstock量化】数据篇之股票基本面数据(https://mp.weixin.qq.com/s/XznS8hFMEa47x1IElZXJaA)
【qstock量化】数据篇之宏观指标和财经新闻文本(https://mp.weixin.qq.com/s/vq7BJUCdHMkcgJYjsErpYQ)
【qstock量化】动态交互数据可视化(https://mp.weixin.qq.com/s/zmY4gsPDQ6xDDpC-fVBUPg)
更多干货请关注微信公众号：Python金融量化
Raw data
{
"_id": null,
"home_page": "https://github.com/tkfy920/qstock",
"name": "qstock",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "pip,qstock",
"author": "Jinyi Zhang",
"author_email": "723195276@qq.com",
"download_url": "https://files.pythonhosted.org/packages/4d/17/863687c9f8a66f326d1761d417f17849fac0d59b9f965d5ebfe7e718ee3c/qstock-1.3.6.tar.gz",
"platform": "any",
"description": "qstock\u7531\u201cPython\u91d1\u878d\u91cf\u5316\u201d\u516c\u4f17\u53f7\u5f00\u53d1\uff0c\u8bd5\u56fe\u6253\u9020\u6210\u4e2a\u4eba\u91cf\u5316\u6295\u7814\u5206\u6790\u5f00\u6e90\u5e93\uff0c\u76ee\u524d\u5305\u62ec\u6570\u636e\u83b7\u53d6\uff08data\uff09\u3001\u53ef\u89c6\u5316(plot)\u3001\u9009\u80a1(stock)\u548c\u91cf\u5316\u56de\u6d4b\uff08backtest\uff09\u56db\u4e2a\u6a21\u5757\u3002\u5176\u4e2d\u6570\u636e\u6a21\u5757\uff08data\uff09\u6570\u636e\u6765\u6e90\u4e8e\u4e1c\u65b9\u8d22\u5bcc\u7f51\u3001\u540c\u82b1\u987a\u3001\u65b0\u6d6a\u8d22\u7ecf\u7b49\u7f51\u4e0a\u516c\u5f00\u6570\u636e\uff0c\u6570\u636e\u722c\u866b\u90e8\u5206\u53c2\u8003\u4e86\u73b0\u6709\u91d1\u878d\u6570\u636e\u5305tushare\u3001akshare\u548cefinance\u3002qstock\u81f4\u529b\u4e8e\u4e3a\u7528\u6237\u63d0\u4f9b\u66f4\u52a0\u7b80\u6d01\u548c\u89c4\u6574\u5316\u7684\u91d1\u878d\u5e02\u573a\u6570\u636e\u63a5\u53e3\u3002\u53ef\u89c6\u5316\u6a21\u5757\u57fa\u4e8eplotly.express\u548cpyecharts\u5305\uff0c\u4e3a\u7528\u6237\u63d0\u4f9b\u57fa\u4e8eweb\u7684\u4ea4\u4e92\u56fe\u5f62\u7b80\u5355\u64cd\u4f5c\u63a5\u53e3\uff1b\u9009\u80a1\u6a21\u5757\u63d0\u4f9b\u4e86\u540c\u82b1\u987a\u7684\u6280\u672f\u9009\u80a1\u548c\u516c\u4f17\u53f7\u7b56\u7565\u9009\u80a1\uff0c\u5305\u62ecRPS\u3001MM\u8d8b\u52bf\u3001\u8d22\u52a1\u6307\u6807\u3001\u8d44\u91d1\u6d41\u6a21\u578b\u7b49\uff0c\u56de\u6d4b\u6a21\u5757\u4e3a\u5927\u5bb6\u63d0\u4f9b\u5411\u91cf\u5316\uff08\u57fa\u4e8epandas\uff09\u548c\u57fa\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u7684\u57fa\u672c\u6846\u67b6\u548c\u6a21\u578b\u3002\r\nqstock\u76ee\u524d\u5728pypi\u5b98\u7f51\u4e0a\u53d1\u5e03\uff0c\u5f00\u6e90\u7b2c\u4e00\u7248\u672c\u4e3a1.1.0\uff0c\u76ee\u524d\u66f4\u65b0\u81f31.3.4.\r\n\r\n\u8bfb\u8005\u76f4\u63a5\u201cpip install qstock \u201d\u5b89\u88c5\uff0c\u6216\u901a\u8fc7'pip install --upgrade qstock'\u8fdb\u884c\u66f4\u65b0\u3002GitHub\u5730\u5740\uff1ahttps://github.com/tkfy920/qstock\u3002\r\n\r\n\u76ee\u524d\u90e8\u5206\u7b56\u7565\u9009\u80a1\u548c\u7b56\u7565\u56de\u6d4b\u529f\u80fd\u4ec5\u4f9b\u77e5\u8bc6\u661f\u7403\u4f1a\u5458\u4f7f\u7528\uff0c\u4f1a\u5458\u53ef\u5728\u77e5\u8bc6\u661f\u7403\u7f6e\u9876\u5e16\u5b50\u4e0a\u4e0a\u83b7\u53d6qstock\u79bb\u7ebf\u5b89\u88c5\u5305\u3002\r\n\r\n\u76f8\u5173\u6559\u7a0b\uff1a\r\n\r\n\u3010qstock\u5f00\u6e90\u4e86\u3011\u6570\u636e\u7bc7\u4e4b\u884c\u60c5\u4ea4\u6613\u6570\u636e(https://mp.weixin.qq.com/s/p1lEsBVhrm5ej3YZl0BhOw)\r\n\u3010qstock\u6570\u636e\u7bc7\u3011\u884c\u4e1a\u6982\u5ff5\u677f\u5757\u4e0e\u8d44\u91d1\u6d41(https://mp.weixin.qq.com/s/hj4yuqFDBidAeSRucz6TwQ)\r\n\u3010qstock\u91cf\u5316\u3011\u6570\u636e\u7bc7\u4e4b\u80a1\u7968\u57fa\u672c\u9762\u6570\u636e(https://mp.weixin.qq.com/s/XznS8hFMEa47x1IElZXJaA)\r\n\u3010qstock\u91cf\u5316\u3011\u6570\u636e\u7bc7\u4e4b\u5b8f\u89c2\u6307\u6807\u548c\u8d22\u7ecf\u65b0\u95fb\u6587\u672c(https://mp.weixin.qq.com/s/vq7BJUCdHMkcgJYjsErpYQ)\r\n\u3010qstock\u91cf\u5316\u3011\u52a8\u6001\u4ea4\u4e92\u6570\u636e\u53ef\u89c6\u5316(https://mp.weixin.qq.com/s/zmY4gsPDQ6xDDpC-fVBUPg)\r\n \r\n\u66f4\u591a\u5e72\u8d27\u8bf7\u5173\u6ce8\u5fae\u4fe1\u516c\u4f17\u53f7\uff1aPython\u91d1\u878d\u91cf\u5316\r\n",
"bugtrack_url": null,
"license": "MIT Licence",
"summary": "Quantitative finance and stock analysis using Python",
"version": "1.3.6",
"project_urls": {
"Homepage": "https://github.com/tkfy920/qstock"
},
"split_keywords": [
"pip",
"qstock"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "4d17863687c9f8a66f326d1761d417f17849fac0d59b9f965d5ebfe7e718ee3c",
"md5": "fa7b27ad30cbccb4d9e681af928253af",
"sha256": "f7e57ac103e42290135c3e49ae89bcd97fcfe36420f93a72be101fa94569e810"
},
"downloads": -1,
"filename": "qstock-1.3.6.tar.gz",
"has_sig": false,
"md5_digest": "fa7b27ad30cbccb4d9e681af928253af",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 151850,
"upload_time": "2023-09-01T06:02:05",
"upload_time_iso_8601": "2023-09-01T06:02:05.835340Z",
"url": "https://files.pythonhosted.org/packages/4d/17/863687c9f8a66f326d1761d417f17849fac0d59b9f965d5ebfe7e718ee3c/qstock-1.3.6.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-09-01 06:02:05",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "tkfy920",
"github_project": "qstock",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "qstock"
}
JSON
